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A new multivariate statistical model of repeated events, the Dirichlet-gamma-Poisson model, is shown to account accurately for the multivariate distribution of four types of victimizations reported in city samples of the National Crime Survey. The lifestyle theory of victimization is used to interpret the compounding that defines the model. Parameter estimation, interpretation, and the prediction of future events based on past events are discussed. The model appears to be applicable to a variety of repeated events data.

The article examines the role that the scale of the criterion variable plays in the development and interpretation of univariate models. The discussion begins by reviewing several exploratory data analysis techniques that use relationships between spread and location parameters to suggest power transformations of the criterion vanable. Data from a study of pretrial release illustrate the techniques. An examination of residuals is shown to complement the search for a good transformation. The effect that independent or carrier variables have on untransformed criterion variables is examinedfor power, logit, and arcsin transformations of the criterion variable. The discussion shows how various transformations imply different relationships between criterion and carrier variables. Inverse transformations are bnefly discussed.